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# Models for AudioSet: A Large Scale Dataset of Audio Events
This repository provides models and supporting code associated with
[AudioSet](http://g.co/audioset), a dataset of over 2 million human-labeled
10-second YouTube video soundtracks, with labels taken from an ontology of
more than 600 audio event classes.
AudioSet was
[released](https://research.googleblog.com/2017/03/announcing-audioset-dataset-for-audio.html)
in March 2017 by Google's Sound Understanding team to provide a common
large-scale evaluation task for audio event detection as well as a starting
point for a comprehensive vocabulary of sound events.
For more details about AudioSet and the various models we have trained, please
visit the [AudioSet website](http://g.co/audioset) and read our papers:
* Gemmeke, J. et. al.,
[AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html),
ICASSP 2017
* Hershey, S. et. al.,
[CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html),
ICASSP 2017
If you use any of our pre-trained models in your published research, we ask that
you cite [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html).
If you use the AudioSet dataset or the released embeddings of AudioSet segments,
please cite
[AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html).
## Contact
For general questions about AudioSet and these models, please use the
[[email protected]](https://groups.google.com/forum/#!forum/audioset-users)
mailing list.
For technical problems with the released model and code, please open an issue on
the [tensorflow/models issue tracker](https://github.com/tensorflow/models/issues)
and __*assign to @plakal and @dpwe*__. Please note that because the issue tracker
is shared across all models released by Google, we won't be notified about an
issue unless you explicitly @-mention us (@plakal and @dpwe) or assign the issue
to us.
## Credits
Original authors and reviewers of the code in this package include (in
alphabetical order):
* DAn Ellis
* Shawn Hershey
* Aren Jansen
* Manoj Plakal
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